Individual movements and contact patterns in a Canadian long-term care facility

Contact networks of individuals in healthcare facilities are poorly understood, largely due to the lack of spatio-temporal movement data. A better understanding of such networks of interactions can help improve disease control strategies for nosocomial outbreaks. We sought to determine the spatio-temporal patterns of interactions between individuals using movement data collected in the largest veterans long-term care facility in Canada. We processed close-range contact data generated by the exchange of ultra-low-power radio signals, in a prescribed proximity, between wireless sensors worn by the participants over a two-week period. Statistical analyses of contact and movement data were conducted. We found a clear dichotomy in the contact network and movement patterns between residents and healthcare workers (HCWs) in this facility. Overall, residents tend to have significantly more distinct contacts with the mean of 17.3 (s.d. 3.6) contacts, versus 3.5 (s.d. 2.3) for HCWs (p-value < 10–12), for a longer duration of time (with mean contact duration of 8 minutes for resident-resident pair versus 4.6 minutes for HCW-resident pair) while being less mobile than HCWs. Analysis of movement data and clustering coefficient of the hourly aggregated network indicates that the contact network is loosely connected (mean clustering coefficient: 0.25, interquartile range 0–0.40), while being highly structured. Our findings bring quantitative insights regarding the contact network and movements in a long-term care facility, which are highly relevant to infer direct human-to-human and indirect (i.e., via the environment) disease transmission processes. This data-driven quantification is essential for validating disease dynamic models, as well as decision analytic methods to inform control strategies for nosocomial infections

Improve Performance Wireless Sensor Network Localization using RSSI and AEMM

Improve wireless sensor network localisation performance using RSSI and an advanced error minimisation method (AEMM). WSNs remain domain-specific and are typically deployed to support a single application. However, as WSN nodes become more powerful, it becomes increasingly important to investigate how multiple applications can share the same WSN infrastructure. Virtualisation is a technology that may allow for this sharing. The issues surrounding wireless sensor node localisation estimation are still being researched. There are a large number of Wireless Sensor Networks (WSNs) with limited computing, sensing, and energy capabilities. Localisation is one of the most important topics in wireless sensor networks (WSNs) because location information is typically useful for many applications. The locations of anchor nodes and the distances between neighbouring nodes are the primary data in a localisation process. The complexity and diversity of current and future wireless detector network operations drive this. Several single schemes have been proposed and studied for position estimation, each with advantages and limitations. Nonetheless, current methods for evaluating the performance of wireless detector networks are heavily focused on a single private or objective evaluation. Accurate position information in a wireless detector network is critical for colourful arising operations (WSN). It is critical to reducing the goods of noisy distance measures to improve localisation accuracy. Existing works (RSSI) are detailed and critically evaluated, with a higher error rate using a set of scenario requirements. Our proposed method (AEMM) is critical for detecting and dealing with outliers in wireless sensor networks to achieve a low localisation error rate. The proposed method (AEMM) for localisation and positioning nodes in wireless sensor networks supported by IOT and discovering the appropriate position of several nodes addresses all of the issues in WSN

The Effect of Individual Movements and Interventions on the Spread of Influenza in Long-Term Care Facilities

Background. Nosocomial influenza poses a serious risk among residents of long-term care facilities (LTCFs). Objective. We sought to evaluate the effect of resident and staff movements and contact patterns on the outcomes of various intervention strategies for influenza control in an LTCF. Methods. We collected contact frequency data in Canada's largest veterans' LTCF by enroling residents and staff into a study that tracked their movements through wireless tags and signal receivers. We analyzed and fitted the data to an agent-based simulation model of influenza infection, and performed Monte-Carlo simulations to evaluate the benefit of antiviral prophylaxis and patient isolation added to standard (baseline) infection control practice (i.e., vaccination of residents and staff, plus antiviral treatment of residents with symptomatic infection). Results. We calibrated the model to attack rates of 20%, 40%, and 60% for the baseline scenario. For data-driven movements, we found that the largest reduction in attack rates (12.5% to 27%; ANOVA P 0.2) among residents. In contrast, parameterizing the model with random movements yielded different results, suggesting that the highest benefit was achieved through patient isolation (69.6% to 79.6%; ANOVA P <0.001) while the additional benefit of prophylaxis was negligible in reducing the cumulative number of infections. Conclusions. Our study revealed a highly structured contact and movement patterns within the LTCF. Accounting for this structureinstead of assuming randomnessin decision analytic methods can result in substantially different predictions

Assinatura de objectos em rádio frequência

Mestrado em Engenharia Eletrónica e TelecomunicaçõesThe RF signature can be consider as a fingerprint of an object when submitted to electromagnetic radiation. Based on this concept, the initial goal of this work was to elaborate a comparative analysis of the Radio Frequency signature of different materials. Through the design of a prototype based on an adapted Wi-Fi network was developed an innovative system capable of distinguishing materials with the analysis of their interference in the propagated channel. In order to refine this distinction was utilized a signal processing tool, the Wavelet Transform. This technique serve as support tool of the system for a better differentiation of the studied targets. The versatility of this concept was proved through the analysis of signatures of static targets like metal, wood and plastic, as well as moving targets, giving the example of a moving human. Due to the promising results obtained, the initial objective of the work was expanded being also presented in this document the concept of intruder detection through a Wi-Fi network by the analysis of the Wavelet coefficients.A Assinatura em Rádio Frequência pode ser considerada como a impressão digital que um objeto manifesta quando submetido a radiação eletromagnética. O objetivo inicial deste trabalho era a elaboração de uma análise comparativa das assinaturas em Rádio Frequência de diferentes materiais. Tendo por base uma rede Wi-Fi adaptada, foi desenvolvido um sistema inovador capaz de distinguir materiais pela análise da interferência dos mesmos no canal de propagação. Com vista a melhorar o desempenho do protótipo inicial, o sinal recebido foi processado através da Transformada de Wavelet. Esta técnica serviu como ferramenta de suporte do sistema para a obtenção de uma diferenciação mais clara dos alvos estudados. Demonstrando a versatilidade deste conceito foram avaliadas as assinaturas de alvos estáticos como o metal, madeira e plástico bem como de alvos móveis dando, como exemplo, uma pessoa em movimento. Devido aos resultados promissores obtidos, o objetivo inicial do sistema foi alargado estando também presente neste documento o conceito de deteção de intrusos através de uma rede Wi-Fi pela análise dos coeficientes de Wavelet

INDOOR LOCATION TRACKING AND ORIENTATION ESTIMATION USING A PARTICLE FILTER, INS, AND RSSI

With the advent of wireless sensor technologies becoming more and more common-place in wearable devices and smartphones, indoor localization is becoming a heavily researched topic. One such application for this topic is in the medical field where wireless sensor devices that are capable of monitoring patient vitals and giving accurate location estimations allow for a less intrusive environment for nursing home patients. This project explores the usage of using received signal strength indication (RSSI) in conjunction with an inertial navigation system (INS) to provide location estimations without the use of GPS in a Particle Filter with a small development microcontroller and base station. The paper goes over the topics used in this thesis and the results

Automated linear regression tools improve RSSI WSN localization in multipath indoor environment

Received signal strength indication (RSSI)-based localization is emerging in wireless sensor networks (WSNs). Localization algorithms need to include the physical and hardware limitations of RSSI measurements in order to give more accurate results in dynamic real-life indoor environments. In this study, we use the Interdisciplinary Institute for Broadband Technology real-life test bed and present an automated method to optimize and calibrate the experimental data before offering them to a positioning engine. In a preprocessing localization step, we introduce a new method to provide bounds for the range, thereby further improving the accuracy of our simple and fast 2D localization algorithm based on corrected distance circles. A maximum likelihood algorithm with a mean square error cost function has a higher position error median than our algorithm. Our experiments further show that the complete proposed algorithm eliminates outliers and avoids any manual calibration procedure

Localization Of Sensors In Presence Of Fading And Mobility

The objective of this dissertation is to estimate the location of a sensor through analysis of signal strengths of messages received from a collection of mobile anchors. In particular, a sensor node determines its location from distance measurements to mobile anchors of known locations. We take into account the uncertainty and fluctuation of the RSS as a result of fading and take into account the decay of the RSS which is proportional to the transmitter-receiver distance power raised to the PLE. The objective is to characterize the channel in order to derive accurate distance estimates from RSS measurements and then utilize the distance estimates in locating the sensors. To characterize the channel, two techniques are presented for the mobile anchors to periodically estimate the channel\u27s PLE and fading parameter. Both techniques estimate the PLE by solving an equation via successive approximations. The formula in the first is stated directly from MLE analysis whereas in the second is derived from a simple probability analysis. Then two distance estimates are proposed, one based on a derived formula and the other based on the MLE analysis. Then a location technique is proposed where two anchors are sufficient to uniquely locate a sensor. That is, the sensor narrows down its possible locations to two when collects RSS measurements transmitted by a mobile anchor, then uniquely determines its location when given a distance to the second anchor. Analysis shows the PLE has no effect on the accuracy of the channel characterization, the normalized error in the distance estimation is invariant to the estimated distance, and accurate location estimates can be achieved from a moderate sample of RSS measurements

A wireless sensor network approach for estimating individual task time

Most existing methods for generating individual task time estimates in production systems where jobs move through various manually operated workstations remain tedious and time consuming. For existing systems, average task times may vary considerably from prior estimates causing system inefficiencies, poor scheduling and poor planning decisions. Accurate and up-to-date estimates are also extremely valuable when planning and designing future assembly lines. In this study, a wireless sensor network (WSN) based indoor positioning system (IPS) is proposed as a potential alternative for estimating individual task times. Several network design factors were varied in the experiments conducted to assess the ability of the WSN-based IPS to determine an operator’s position within the workstation so that the total production time can be allocated to the appropriate tasks. The main response variable utilized to determine the location of the operator within the workstation was the link quality indicator (LQI). Accurately measuring LQI levels is not a straightforward process since radio frequency (RF) signals can change over time depending on many conditions, including physical obstructions and electromagnetic interference (EMI). The results show that a WSN-based IPS is a viable approach to estimating individual task times. Additionally, the analysis of the experimental data showed that certain WSN design factors need to be set carefully to ensure good quality in the estimation of individual task times

Experimental Analysis of RSSI-based Location Estimation in Wireless Sensor Networks

Abstract—With a widespread increase in the number of mobile wireless systems and applications, the need for location aware services has risen at a very high pace in the last few years. Much research has been done for the development of new models for location aware systems, but most of it has primarily used the support of 802.11 wireless networks. Less work has been done towards an exhaustive error analysis of the underlying theories and models, especially in an indoor environment using a wireless sensor network. We present a thorough analysis of the Radio Signal Strength (RSS) model for distance estimation in wireless sensor networks through an empirical quantification of error metrics. Further on the basis of this experimental analysis, we implement a k- nearest signal space neighbor match algorithm for location estimation, and evaluate some crucial control parameters using which this technique can be adapted to different cases and scenarios, to achieve finer and more precise location estimates. I

A state communication and software switching module and thin middleware layer for reconfiguration management in reconfigurable manufacturing systems.

M. Sc. Eng. University of KwaZulu-Natal, Durban 2015.Reconfigurable Manufacturing Systems are a new area of operations and manufacturing research. The global need for production systems which can react rapidly to dynamic markets has increased in the last decade and will continue to drive changes in the manufacturing industry. The further development of RMS technologies is therefore highly important for future industries. The Reconfiguration Management and Middleware System (RMMS) developed in this research aimed to form a hardware-supported middleware technology which allows for the fast and seamless ramp-up of heterogeneous machine controllers on a newly reconfigured factory floor. The goal was to allow for the autonomous assignment and switching of software routines on machine controllers after a physical reconfiguration, thereby speeding up the ramp-up of the system. The technology was based on a recorded literature review and fits into the paradigm of RMS. The RMMS was developed not as a traditional software-heavy layer, but as a thin layer of software assisted by interactive mechatronic hardware, designed to remove heterogeneity in the control software. The system design was based on research into areas of engineering and operations management and followed the Mechatronic design approach. The literature led to a technology that takes the entire RMS paradigm into account and the development was conducted in conjunction with experiments to verify the individual functionality of each sub-system and ensure the overall system’s success. The RMMS uses hardware to handle heterogeneity and uses a positioning system (developed by the author) along with an intelligent processing system (a clustering algorithm and artificial intelligence engine) to construct data into a factory floor model. The positioning system, when assisted by the intelligence, operates at an accuracy of over 90%, which is comparable to commercial positioning techniques which cost over ten times more. The RMMS used the developed model to, autonomously and wirelessly, assign new programs to machine controllers after a physical reconfiguration, to complete a factory reconfiguration. The system was verified through practical scenarios constructed in the Mechatronics laboratory. Realistic reconfiguration operations were performed and the RMMS was required to detect changes in the factory floor and respond by assigning new, appropriate, software routines to each machine controller in the system. Experiments have proved that the system was capable of re-establishing operations in under half an hour, as opposed to a full day using manual techniques. The system has accurately switched between control routines based on the physical state of the factory floor, which amounts to control reconfiguration. The reconfiguration of factory floor control was successful in four out of four factory layouts tested and therefore successfully does a job no commercially available system can do